Added an example program for the pegasos algorithm.

--HG--
extra : convert_revision : svn%3Afdd8eb12-d10e-0410-9acb-85c331704f74/trunk%402912

examples/CMakeLists.txt

@@ -61,6 +61,7 @@ add_example(sockets_ex_2)
 add_example(sockstreambuf_ex)
 add_example(std_allocator_ex)
 add_example(svm_ex)
+add_example(svm_pegasos_ex)
 add_example(threaded_object_ex)
 add_example(thread_function_ex)
 add_example(thread_pool_ex)

examples/svm_pegasos_ex.cpp

@@ -0,0 +1,152 @@
+// The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
+/*
+
+    This is an example illustrating the use of the dlib C++ library's
+    implementation of the pegasos algorithm for online training of support 
+    vector machines.   
+
+    This example creates a simple binary classification problem and shows
+    you how to train a support vector machine on that data.
+
+    The data used in this example will be 2 dimensional data and will
+    come from a distribution where points with a distance less than 10
+    from the origin are labeled +1 and all other points are labeled
+    as -1.
+        
+*/
+
+
+#include <iostream>
+#include <vector>
+#include "dlib/svm.h"
+
+using namespace std;
+using namespace dlib;
+
+
+int main()
+{
+    // The svm functions use column vectors to contain a lot of the data on which they they 
+    // operate. So the first thing we do here is declare a convenient typedef.  
+
+    // This typedef declares a matrix with 2 rows and 1 column.  It will be the
+    // object that contains each of our 2 dimensional samples.   (Note that if you wanted 
+    // more than 2 features in this vector you can simply change the 2 to something else.
+    // Or if you don't know how many features you want until runtime then you can put a 0
+    // here and use the matrix.set_size() member function)
+    typedef matrix<double, 2, 1> sample_type;
+
+
+    // This is a typedef for the type of kernel we are going to use in this example.
+    // In this case I have selected the radial basis kernel that can operate on our
+    // 2D sample_type objects
+    typedef radial_basis_kernel<sample_type> kernel_type;
+
+
+    // Here we create an instance of the pegasos svm trainer object we will be using.
+    svm_pegasos<kernel_type> trainer;
+    // Here we setup the parameters to this object.  See the dlib documentation for a 
+    // description of what these parameters are. 
+    trainer.set_lambda(0.00001);
+    trainer.set_kernel(kernel_type(0.005));
+    trainer.set_tolerance(0.2);
+
+    std::vector<sample_type> samples;
+    std::vector<double> labels;
+
+    // make an instance of a sample matrix so we can use it below
+    sample_type sample, center;
+
+    center = 20, 20;
+
+    // Now lets go into a loop and randomly generate 1000 samples.
+    srand(time(0));
+    for (int i = 0; i < 1000; ++i)
+    {
+        // Make a random sample vector. 
+        sample = randm(2,1)*40 - center;
+
+        // Now if that random vector is less than 10 units from the origin then it is in 
+        // the +1 class.
+        if (length(sample) <= 10)
+        {
+            // let the svm_pegasos learn about this sample
+            trainer.train(sample,+1);
+
+            // save this sample so we can use it with the batch training examples below
+            samples.push_back(sample);
+            labels.push_back(1);
+        }
+        else
+        {
+            // let the svm_pegasos learn about this sample
+            trainer.train(sample,-1);
+
+            // save this sample so we can use it with the batch training examples below
+            samples.push_back(sample);
+            labels.push_back(-1);
+        }
+    }
+
+    // Now we have trained our SVM.  Lets see how well it did.  
+    // each of these statements prints out the output of the SVM given a particular sample.  
+    // The SVM outputs a number > 0 if a sample is predicted to be in the +1 class and < 0 
+    // if a sample is predicted to be in the -1 class.
+
+    sample(0) = 3.123;
+    sample(1) = 4;
+    cout << "This is a +1 example, its SVM output is: " << trainer(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 9.3545;
+    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 0;
+    cout << "This is a -1 example, its SVM output is: " << trainer(sample) << endl;
+
+
+
+
+
+    // The previous parts of this example program showed you how to perform online training
+    // with the pegasos algorithm.  But it is often the case that you have a dataset and you 
+    // just want to perform batch learning on that dataset and get the resulting decision
+    // function.  To support this the dlib library provides functions for converting an online
+    // training object like svm_pegasos into a batch training object.  
+
+    // First lets clear out anything in the trainer object.
+    trainer.clear();
+
+    // Now to begin with, you might want to compute the cross validation score of a trainer object
+    // on your data.  To do this you should use the batch() function to convert the svm_pegasos object
+    // into a batch training object.  Note that the second argument to batch() is the minimum 
+    // learning rate the trainer object must report for the batch() function to consider training
+    // complete.  So smaller values of this parameter cause training to take longer but may result
+    // in a more accurate solution. 
+    // Here we perform 4-fold cross validation and print the results
+    cout << "cross validation: " << cross_validate_trainer(batch(trainer,1.0), samples, labels, 4);
+
+    // Here is an example of creating a decision function.  Note that we have used the verbose_batch()
+    // function instead of batch() as above.  They do the same things except verbose_batch() will
+    // print status messages to standard output while training is under way.
+    decision_function<kernel_type> df = verbose_batch(trainer,0.1).train(samples, labels);
+
+    // At this point we have obtained a decision function from the above batch mode training.
+    // Now we can use it on some test samples exactly as we did above.
+
+    sample(0) = 3.123;
+    sample(1) = 4;
+    cout << "This is a +1 example, its SVM output is: " << df(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 9.3545;
+    cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
+
+    sample(0) = 13.123;
+    sample(1) = 0;
+    cout << "This is a -1 example, its SVM output is: " << df(sample) << endl;
+
+
+}
+